edu.cmu.ml.rtw.pra.graphs

SyntheticDataCreator

class SyntheticDataCreator extends AnyRef

Things this doesn't capture very well: - Recursion (if you want to demonstrate where PRA fails with respect to ProPPR) - Mutual exclusivity of certain relations (and maybe other kinds of relation metadata), though PRA doesn't do a great job exploiting this right now, anyway - Characteristics of actual data. It'd be nice if you could generate some synthetic data that was based off of what you see in Freebase, for instance, though I'm not really sure how to do that.

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Instance Constructors

  1. new SyntheticDataCreator(base_dir: String, params: JValue, outputter: Outputter, fileUtil: FileUtil = new com.mattg.util.FileUtil())

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    @throws( ... )

  8. def createRelationSet(): Unit

  9. val data_file: String

  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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  13. implicit val formats: DefaultFormats.type

  14. def generateNoiseInstances(): Set[(Int, String, Int)]

  15. def generateOverlappingInstances(relation_set: (Array[String], Array[String])): Set[(Int, String, Int)]

  16. def generatePraInstance(name: String, rules: Seq[(Seq[Int], Double)], negative_rules: Seq[(Seq[Int], Double)], isTraining: Boolean): (Set[(Int, String, Int, Boolean)], Set[(Int, String, Int, Boolean)], Set[(Int, String, Int)])

  17. def generatePraRelations(relation_index: Int): (String, (Seq[(Seq[Int], Double)], Seq[(Seq[Int], Double)]))

  18. def generateRelationInstances(pra_relation: (String, (Seq[(Seq[Int], Double)], Seq[(Seq[Int], Double)]))): (Seq[(Int, String, Int, Boolean)], Seq[(Int, String, Int, Boolean)], Seq[(Int, String, Int)])

  19. def generateSupportingEdges(source: Int, target: Int, isTraining: Boolean)(rule: (Seq[Int], Double)): Set[(Int, String, Int)]

  20. final def getClass(): Class[_]

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  21. def getConcreteBaseRelation(index: Int, isTraining: Boolean): String

  22. def hashCode(): Int

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  23. val in_progress_file: String

  24. final def isInstanceOf[T0]: Boolean

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  25. val max_rule_length: Int

  26. val min_rule_length: Int

  27. val name: String

  28. final def ne(arg0: AnyRef): Boolean

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. val num_base_relation_noise_instances: Int

  32. val num_base_relation_overlapping_instances: Int

  33. val num_base_relation_testing_duplicates: Int

  34. val num_base_relation_training_duplicates: Int

  35. val num_base_relations: Int

  36. val num_entities: Int

  37. val num_negatives_per_positive: Int

  38. val num_noise_relation_instances: Int

  39. val num_noise_relations: Int

  40. val num_pra_relation_testing_instances: Int

  41. val num_pra_relation_training_instances: Int

  42. val num_pra_relations: Int

  43. val num_rules: Int

  44. def outputRelationSet(all_instances: Seq[(Int, String, Int)]): Unit

  45. def outputRules(rules: Seq[(String, Seq[Int], Double, Boolean)]): Unit

  46. def outputSplitFiles(training_instances: Set[(Int, String, Int, Boolean)], testing_instances: Set[(Int, String, Int, Boolean)], pra_relations: Set[String]): Unit

  47. val param_file: String

  48. val r: Random

  49. def rekeyByRelation(instances: Set[(Int, String, Int, Boolean)]): Map[String, Set[(Int, Int, Boolean)]]

  50. val relation_set_dir: String

  51. val relation_sets: Array[(Array[String], Array[String])]

  52. val rule_prob_mean: Double

  53. val rule_prob_stddev: Double

  54. val split_dir: String

  55. final def synchronized[T0](arg0: ⇒ T0): T0

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  56. def toString(): String

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  57. final def wait(): Unit

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  58. final def wait(arg0: Long, arg1: Int): Unit

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  59. final def wait(arg0: Long): Unit

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